Zobrazeno 1 - 10
of 272
pro vyhledávání: '"Senthilnath, J"'
Autor:
Aung, Aye Phyu Phyu, Wang, Xinrun, Wang, Ruiyu, Chan, Hau, An, Bo, Li, Xiaoli, Senthilnath, J.
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GA
Externí odkaz:
http://arxiv.org/abs/2410.04764
Accurate simulations of materials at long-time and large-length scales have increasingly been enabled by Machine-learned Interatomic Potentials (MLIPs). There have been increasing interest on improving the robustness of such models. To this end, we e
Externí odkaz:
http://arxiv.org/abs/2407.06615
Autor:
Senthilnath, J., Zhou, Bangjian, Ng, Zhen Wei, Aggarwal, Deeksha, Dutta, Rajdeep, Yoon, Ji Wei, Aung, Aye Phyu Phyu, Wu, Keyu, Wu, Min, Li, Xiaoli
In the realm of sequential decision-making tasks, the exploration capability of a reinforcement learning (RL) agent is paramount for achieving high rewards through interactions with the environment. To enhance this crucial ability, we propose SAQN, a
Externí odkaz:
http://arxiv.org/abs/2402.11604
Autor:
Senthilnath, J., Bhattiprolu, Adithya, Singh, Ankur, Zhou, Bangjian, Wu, Min, Benediktsson, Jón Atli, Li, Xiaoli
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM
Externí odkaz:
http://arxiv.org/abs/2402.09167
In this paper, we propose a novel inverse parameter estimation approach called Bayesian optimized physics-informed neural network (BOPINN). In this study, a PINN solves the partial differential equation (PDE), whereas Bayesian optimization (BO) estim
Externí odkaz:
http://arxiv.org/abs/2312.14064
Autor:
Yoon, Ji Wei, Kumar, Adithya, Kumar, Pawan, Hippalgaonkar, Kedar, Senthilnath, J, Chellappan, Vijila
Publikováno v:
Knowledge-Based Systems 295C (2024) 111812
The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measureme
Externí odkaz:
http://arxiv.org/abs/2308.04103
With high device integration density and evolving sophisticated device structures in semiconductor chips, detecting defects becomes elusive and complex. Conventionally, machine learning (ML)-guided failure analysis is performed with offline batch mod
Externí odkaz:
http://arxiv.org/abs/2303.07062
Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role in understanding the short-duration transient response of composite structures. The forward physics-based models are utiliz
Externí odkaz:
http://arxiv.org/abs/2212.06365
In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil mois
Externí odkaz:
http://arxiv.org/abs/2211.01958
This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communi
Externí odkaz:
http://arxiv.org/abs/2207.00725